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Cross-Dictionary Linking at Sense Level with a Double-Layer Classifier

Authors Roser Saurí, Louis Mahon, Irene Russo, Mironas Bitinis

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Author Details

Roser Saurí
  • Dictionaries Technology Group, Oxford University Press, UK
Louis Mahon
  • Dictionaries Technology Group, Oxford University Press, UK
  • Oxford University, UK
Irene Russo
  • Dictionaries Technology Group, Oxford University Press, UK
  • ILC A. Zampolli - CNR, Pisa, Italy
Mironas Bitinis
  • Dictionaries Technology Group, Oxford University Press, UK

Acknowledgements

We are very grateful to Charlotte Buxton and Rebecca Juganaru, the expert lexicographers who have contributed all the dictionary knowledge we were lacking and have helped with manual annotations. In addition, we want to express our thanks to Richard Shapiro, Will Hunter and Sophie Wood for their great support in different aspects of the project. All errors and mistakes are responsibility of the authors.

Cite AsGet BibTex

Roser Saurí, Louis Mahon, Irene Russo, and Mironas Bitinis. Cross-Dictionary Linking at Sense Level with a Double-Layer Classifier. In 2nd Conference on Language, Data and Knowledge (LDK 2019). Open Access Series in Informatics (OASIcs), Volume 70, pp. 20:1-20:16, Schloss Dagstuhl – Leibniz-Zentrum für Informatik (2019)
https://doi.org/10.4230/OASIcs.LDK.2019.20

Abstract

We present a system for linking dictionaries at the sense level, which is part of a wider programme aiming to extend current lexical resources and to create new ones by automatic means. One of the main challenges of the sense linking task is the existence of non one-to-one mappings among senses. Our system handles this issue by addressing the task as a binary classification problem using standard Machine Learning methods, where each sense pair is classified independently from the others. In addition, it implements a second, statistically-based classification layer to also model the dependence existing among sense pairs, namely, the fact that a sense in one dictionary that is already linked to a sense in the other dictionary has a lower probability of being linked to a further sense. The resulting double-layer classifier achieves global Precision and Recall scores of 0.91 and 0.80, respectively.

Subject Classification

ACM Subject Classification
  • Computing methodologies → Lexical semantics
  • Computing methodologies → Language resources
  • Computing methodologies → Supervised learning by classification
Keywords
  • Word sense linking
  • word sense mapping
  • lexical translation
  • lexical resources
  • language data construction
  • multilingual data
  • data integration across languages

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